We first adopt the theory of CEEMDAN [1]} to decompose the original non-stationary traffic flow signals into several IMF sub-signals with different frequencies. We then utilize SE (sample entropy) to measure the nonlinear complexity of the IMF subsequence to merge similar sub-signals for computation reduction. For the prediction model, we deploy the LSTM model with GWO optimization to improve the prediction accuracy and decrease the training duration. Finally, we will extract the spatiotemporal features from the output of LSTM to avoid the autocorrelation problem. The pipeline of our approach is demonstrated in Fig. REF .
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